---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: "public class HelloWorld {\n    public static void main(String[] args) {"
  example_title: Hello world
  group: Java
---


# JavaCoder


##  Table of Contents

1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)

## Model Summary

The JavaCoder models are !B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135),  and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens. 

- **Repository:** 
- **Project Website:** 
- **Paper:** 
- **Point of Contact:** 
- **Languages:** 80+ Programming languages

## Use

### Intended use

The model was trained on GitHub code. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the [Tech Assistant prompt](https://huggingface.co/datasets/bigcode/ta-prompt) you can turn it into a capable technical assistant.

**Feel free to share your generations in the Community tab!**

### Generation
```Java
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "infosys/javacoder-1b"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

inputs = tokenizer.encode("public class HelloWorld {\n    public static void main(String[] args) {", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```

### Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:

```Java
input_text = "<fim_prefix>public class HelloWorld {\n    public static void main(String[] args) {<fim_suffix>}\n}<fim_middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```